Marine target detection technology plays an important role in sea surface monitoring, sea area management, ship collision avoidance, and other fields. Traditional marine target detection algorithms cannot meet the requirements of accuracy and speed. This article uses the advantages of deep learning in big data feature learning to propose the YOLOv4 marine target detection method fused with a convolutional attention module. Marine target detection datasets were collected and produced and marine targets were divided into ten categories, including speedboat, warship, passenger ship, cargo ship, sailboat, tugboat, and kayak. Aiming at the problem of insufficient detection accuracy of YOLOv4’s self-built marine target dataset, a convolutional attention module is added to the YOLOv4 network to increase the weight of useful features while suppressing the weight of invalid features to improve detection accuracy. The experimental results show that the improved YOLOv4 has higher detection accuracy than the original YOLOv4, and has better detection results for small targets, multiple targets, and overlapping targets. The detection speed meets the real-time requirements, verifying the effectiveness of the improved algorithm.

https://www.mdpi.com/2073-8994/13/4/623/pdf

Improved YOLOv4 Marine Target Detection Combined with CBAM

Real-time and accurate detection of ships plays a vital role in ensuring navigation safety and ship supervision. Aiming at the problems of large parameters, large computation quantity, poor real-time performance, and high requirements for memory and computing power of the current ship detection model, this paper proposes a ship target detection algorithm MC-YOLOv5s based on YOLOv5s. First, the MobileNetV3-Small lightweight network is used to replace the original feature extraction backbone network of YOLOv5s to improve the detection speed of the algorithm. And then, a more efficient CNeB is designed based on the ConvNeXt-Block module of the ConvNeXt network to replace the original feature fusion module of YOLOv5s, which improves the spatial interaction ability of feature information and further reduces the complexity of the model. The experimental results obtained from the training and verification of the MC-YOLOv5s algorithm show that, compared with the original YOLOv5s algorithm, MC-YOLOv5s reduces the number of parameters by 6.98 MB and increases the mAP by about 3.4%. Even compared with other lightweight detection models, the improved model proposed in this paper still has better detection performance. The MC-YOLOv5s has been verified in the ship visual inspection and has great application potential. The code and models are publicly available at https://github.com/sakura994479727/datas.

/pdf/a-lightweight-ship-target-detection-model-based-on-improved-257n7qkv.pdf

A lightweight ship target detection model based on improved YOLOv5s algorithm

Maritime traffic community has paid a huge amount of focuses to establish maritime intelligent transportation infrastructure for the purpose of enhancing maritime traffic safety and efficiency. Maritime surveillance video is considered as a type of fundamental data sources for establishing intelligent maritime transportation infrastructure towards smart ship era. To that end, the study proposes an aggregated deep learning model-supported ship imaging trajectory extraction framework. The proposed framework starts by detecting ships from maritime images via a novel You Only Look Once (YOLO) model. More specifically, the proposed ship trajectory extraction framework obtains ship positions in a frame-by-frame manner via the proposed poly-YOLO module. Then, the proposed model maps ship positions in neighboring consecutive maritime images via an Enhanced Deep Sort (EDS) module. Experimental results suggest that the proposed ship trajectory extraction model achieves satisfactory performance due to that the average values of index multiple-object tracking accuracy (MOTa), recall rate (Rid) and index aggregated detection accuracy (Aggid) are larger than 89% (which outperform the comparison algorithms). The study can help varied maritime traffic participants obtain accurate on-site traffic situations in the smart ship era. 

Ship imaging trajectory extraction via an aggregated you only look once (YOLO) model

As the security threat and crime rate have been increased all over the globe, the video surveillance system using closed-circuit television (CCTV) has become an essential tool for many security-related applications and is widely used in many areas as a monitoring system. However, most of the data collected by the video surveillance system is used as evidence of objective data after crime and disaster have occurred. And, often time, video surveillance systems tend to be used in a passive manner due to the high cost and human resources. The video surveillance system should actively respond to detect crime and accidents in advance through real-time monitoring and immediately transmit data in case of an accident. This study proposes developing an intelligent video surveillance system that can actively monitor in real-time without human input. In solving the problems of the existing video surveillance system, deep learning technology will be carried through the data processing model design to visualize data for crime detection after building an artificial intelligence server and video surveillance camera. In addition, this design proposes an intelligent surveillance system to quickly and effectively detect crimes by sending a video image and notification message to the web through real-time processing.

Design of an intelligent video surveillance system for crime prevention: Applying deep learning technology

Rapid and accurate detection of maritime military targets is of great significance for maintaining national defense security. Few studies have used high-resolution optical images for the detailed classification of maritime military targets. This article, inspired by EfficientDet trackers, presents a method to classify military targets on the sea from high-resolution optical remote sensing images. In the first stage, a multilayer feature extraction network is constructed to extract various features. At the same time, residual connection and dilation convolution are introduced to prevent the deep network features from disappearing. Moreover, we use multilevel attention mechanism approaches to make more effective use of multilayer features. ReLU is introduced to replace the original swish activation function to reduce the computational cost in the pretreatment stage. After this, deep feature fusion networks and prediction networks are constructed to locate and distinguish different types of ships. Different types of ships use different degrees of data expansion methods to solve the problem of sample shortage and imbalance. The multiclassification method is used to solve low classification accuracy caused by little difference between civil and military ships. Experimental results suggested that the proposed method can accurately identify multiple types of military ships.

Multilayer Feature Extraction Network for Military Ship Detection From High-Resolution Optical Remote Sensing Images

Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.

https://www.mdpi.com/2079-9292/8/9/959/pdf

Ship Target Detection Algorithm Based on Improved Faster R-CNN

The invention relates to hull outer plate curved surface forming equipment based on double mechanical arms and an implementation method thereof. A main body of an equipment hardware system adopts a frame structure of a portal frame, two floating six-shaft industrial mechanical arms are mounted on a cross beam of the portal frame, and a flame gun heating device, a water spray cooling device and a distance measuring sensor are mounted at the tail end of each mechanical arm; a camera acquires ship plate shape data; and a touch control console manipulates the entire equipment. The equipment software system integrates an intelligent forming decision support subsystem for the hull outer plate curved surface, a motion control subsystem and the like, the former is used to generate a heating trackand machining parameters, and the latter is used to control the coordinated motion of the double mechanical arms. The equipment provided by the invention can automatically process the curved surface shape of a ship steel plate, and reduces the labor intensity of workers.

Hull outer plate curved surface forming equipment based on double mechanical arms and implementation method thereof

In the process of curved forming for ship’s hull plates, one of the main parameters is to determine the position of the flame path . By research on the theory for curved forming of ship’s surface, the comprehensive database of hull plate surface deformation is established, and the prediction algorithm of hull plate process parameters is put forward by combining the optimization of plate unfolding method and ACA, and the corresponding prediction system of process parameters is developed.

Research on intelligent decision support system of ship’s hull plates curved forming by line heating

an engine room alarm monitoring system is a core part of ship’s Marine Automation System. The level of designing and implementing the system is very crucial for the safety and the sound operations of a ship. Therefore, the system should be effective and reliable. In this paper, the structure of the engine room monitoring and alarm system was designed, including hardware and the software. The program of PLC and SCADA designed by the TIA portal software was employed to implement all monitoring and alarm functions.

Design and Implementation of a new type of Marine Engine Room Alarm Monitoring System

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Wei Wang

Author Tools

Chat about Author

Papers

Ship Target Detection Algorithm Based on Improved Faster R-CNN

Hull outer plate curved surface forming equipment based on double mechanical arms and implementation method thereof

Research on intelligent decision support system of ship’s hull plates curved forming by line heating

Design and Implementation of a new type of Marine Engine Room Alarm Monitoring System